Supply chain planning is essential to the success of many of today's manufacturing firms. Most manufacturing firms rely on supply chain planning in some form to ensure the timely delivery of products in response to customer demands. Typically, supply chain planning is hierarchical in nature, extending from distribution and production planning driven by customer orders, to materials and capacity requirements planning, to shop floor scheduling, manufacturing execution, and deployment of products. Supply chain planning ensures the smooth functioning of different aspects of production, from the ready supply of components to meet production demands to the timely transportation of finished goods from the factory to the customer.
One key aspect in the hierarchical supply chain planning approach is the generation of a mid-term production and distribution plan. This planning is commonly referred to as supply-net-work-planning (SNP), supply network planning (SNP), and the term SNP will be frequently used herein.
One particularly effective tool for supply chain planning is the Advanced Planner and Optimizer (APO) provided by SAP AG. APO belongs to the class of Advanced Planning Systems (APS) and covers all supply chain planning tasks. An integral part of APO is the Supply Network Planning Module. Within the SNP module, different planning methods are available, the most comprehensive being the SNP optimizer. Using the SNP module, a user, such as a supply chain planner or supervisor, generates and maintains an electronic SNP model that defines which entities of a supply chain should be planned and further specifies any constraints that should be respected during optimization.
The model also specifies one or more demands that should be met, either wholly or partially. Demands typically relate to the delivery of some number of products to a particular customer at a particular time. Also, the user generates and maintains a cost model, which is used by the optimizer for evaluating feasible supply chain solutions that ideally meet all the demands subject to all the constraints while requiring minimal supply chain costs. A cost-based optimizer evaluates all feasible supply chain solutions and searches for a global solution that has a minimum cost evaluation. It should be noted here that the term “cost” does not—or at least not necessarily—relate to a monetary value but generally denotes a typically penalty-based parameter utilized by the optimization mechanism to find an optimal solution.
Hence, optimization is performed from a global perspective to allow high quality planning solutions. However, it is sometimes difficult for the user to understand the solution from a “local” perspective, with which users are typically more familiar. For example, a user may not understand why a certain demand was not satisfied within the optimized supply chain. This is particularly true if the optimized supply chain takes into account global constraints with which the user is not familiar. In one global scenario, a demand may not or not completely be satisfied due to limited production capabilities on different levels of a production chain. In another global scenario, a demand may not or not completely be satisfied due to finite capacities in a multilevel distribution chain including the handling, transport and storage of products underlying the demand. In such multilevel optimization contexts, it is often difficult to localize the reason for an individual bottleneck.
Accordingly, it would be desirable to provide an automated mechanism for quickly and efficiently analyzing an optimized supply chain planning problem and for providing a computer-generated explanation of why certain demands could not (or not fully) be met.
It is therefore an object of the present invention to provide an automated explanation technique that is technically compatible with existing APSs such as the APO and that is capable of automatically generating meaningful information for a user of the APS.